Data-Driven Machine-Learning Quantifies Differences in the Voiding Initiation Network in Neurogenic Voiding Dysfunction in Women With Multiple Sclerosis
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초록
Purpose: To quantify the relative importance of brain regions responsible for reduced functional connectivity (FC) in their Voiding Initiation Network in female multiple sclerosis (MS) patients with neurogenic lower urinary tract dysfunction (NLUTD) and voiding dysfunction (VD). A data-driven machine-learning approach is utilized for quantification. Methods: Twenty-seven ambulatory female patients with MS and NLUTD (group 1: voiders, n=15 and group 2: VD, n=12) participated in a functional magnetic resonance imaging (fMRI) voiding study. Brain activity was recorded by fMRI with simultaneous urodynamic testing. The Voiding Initiation Network was identified from averaged fMRI activation maps. Four machine-learning algorithms were employed to optimize the area under curve (AUC) of the receiver-operating characteristic curve. The optimal model was used to identify the relative importance of relevant brain regions. Results: The Voiding Initiation Network exhibited stronger FC for voiders in frontal regions and stronger disassociation in cerebellar regions. Highest AUC values were obtained with ‘random forests’ (0.86) and ‘partial least squares’ algorithms (0.89). While brain regions with highest relative importance (>75%) included superior, middle, inferior frontal and cingulate regions, relative importance was larger than 60% for 186 of the 227 brain regions of the Voiding Initiation Network, indicating a global effect. Conclusions: Voiders and VD patients showed distinctly different FC in their Voiding Initiation Network. Machine-learning is able to identify brain centers contributing to these observed differences. Knowledge of these centers and their connectivity may allow phenotyping patients to centrally focused treatments such as cortical modulation.
Abstract
Purpose: To quantify the relative importance of brain regions responsible for reduced functional connectivity (FC) in their Voiding Initiation Network in female multiple sclerosis (MS) patients with neurogenic lower urinary tract dysfunction (NLUTD) and voiding dysfunction (VD). A data-driven machine-learning approach is utilized for quantification. Methods: Twenty-seven ambulatory female patients with MS and NLUTD (group 1: voiders, n=15 and group 2: VD, n=12) participated in a functional magnetic resonance imaging (fMRI) voiding study. Brain activity was recorded by fMRI with simultaneous urodynamic testing. The Voiding Initiation Network was identified from averaged fMRI activation maps. Four machine-learning algorithms were employed to optimize the area under curve (AUC) of the receiver-operating characteristic curve. The optimal model was used to identify the relative importance of relevant brain regions. Results: The Voiding Initiation Network exhibited stronger FC for voiders in frontal regions and stronger disassociation in cerebellar regions. Highest AUC values were obtained with ‘random forests’ (0.86) and ‘partial least squares’ algorithms (0.89). While brain regions with highest relative importance (>75%) included superior, middle, inferior frontal and cingulate regions, relative importance was larger than 60% for 186 of the 227 brain regions of the Voiding Initiation Network, indicating a global effect. Conclusions: Voiders and VD patients showed distinctly different FC in their Voiding Initiation Network. Machine-learning is able to identify brain centers contributing to these observed differences. Knowledge of these centers and their connectivity may allow phenotyping patients to centrally focused treatments such as cortical modulation.
참고문헌 (15)
- Corcos J, "A urological challenge: voiding dysfunction in multiple sclerosis", Can Urol Assoc J, 7(9-10 Suppl 4), pp.S181~182 (2013)
- Wintner A, "Voiding dysfunction in multiple sclerosis", Semin Neurol, 36, pp.34~40 (2016)
- Stöhrer M, "EAU guidelines on neurogenic lower urinary tract dysfunction", Eur Urol, 56, pp.81~88 (2009)
- Kim YH, "The correlation of urodynamic findings with cranial magnetic resonance imaging findings in multiple sclerosis", J Urol, 159, pp.972~976 (1998)
- Khavari R, "Higher neural correlates in patients with multiple sclerosis and neurogenic overactive bladder following treatment with intradetrusor injection of onabotulinumtoxinA", J Urol, 201, pp.135~140 (2019)
- Tahedl M, "Functional connectivity in multiple sclerosis : recent findings and future directions", Front Neurol, 9, pp.828~ (2018)
- Khavari R, "Similarity of functional connectivity patterns in patients with multiple sclerosis who void spontaneously versus patients with voiding dysfunction", Neurourol Urodyn, 38, pp.239~247 (2019)
- Hasan KM, "Human brain atlas-based multimodal MRI analysis of volumetry, diffusimetry, relaxometry and lesion distribution in multiple sclerosis patients and healthy adult controls : implications for understanding the pathogenesis of multiple sclerosis and consolidation of quantitative MRI results in MS", J Neurol Sci, 313, pp.99~109 (2012)
- Blok BF, "Brain activation during micturition in women", Brain, 121(Pt 11), pp.2033~2042 (1998)
- Blok BF, "A PET study on brain control of micturition in humans", Brain, 120(Pt 1), pp.111~121 (1997)
- Shy M, "Functional magnetic resonance imaging during urodynamic testing identifies brain structures initiating micturition", J Urol, 192, pp.1149~1154 (2014)
- Nour S, "Cerebral activation during micturition in normal men", Brain, 123(Pt 4), pp.781~789 (2000)
- Khavari R, "Functional magnetic resonance imaging with concurrent urodynamic testing identifies brain structures involved in micturition cycle in patients with multiple sclerosis", J Urol, 197, pp.438~444 (2017)
- Kuhtz-Buschbeck JP, "Control of bladder sensations : an fMRI study of brain activity and effective connectivity", Neuroimage, 47, pp.18~27 (2009)
- Kuhtz-Buschbeck JP, "Cortical representation of the urge to void: a functional magnetic resonance imaging study", J Urol, 174(4 Pt 1), pp.1477~1481 (2005)